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Efficient Estimation for Semiparametric Structural Equation Models With Censored Data
Authors
D.Y. Lin
K.Y. Wong
D. Zeng
Publication date
1 January 2018
Publisher
American Statistical Association
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Abstract
Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general class of structural equation models with a semiparametric component for potentially censored survival times. We consider nonparametric maximum likelihood estimation and devise a combined expectation-maximization and Newton-Raphson algorithm for its implementation. We establish conditions for model identifiability and prove the consistency, asymptotic normality, and semiparametric efficiency of the estimators. Finally, we demonstrate the satisfactory performance of the proposed methods through simulation studies and provide an application to a motivating cancer study that contains a variety of genomic variables. Supplementary materials for this article are available online. © 2018, © 2018 American Statistical Association
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Last time updated on 24/11/2020